Experience goods, reinforcement learning, and social networks

This paper explores decision-making processes in experience goods markets, emphasizing how agents learn to choose in situations where value is uncertain until after consumption. The study examines agents employing individual and social learning strategies within a multi-dimensional reinforcement learning framework, particularly in scenarios of repeated choices. Agents in our model gather insights from personal experiences and through recommendations within their social networks. The simulation results highlight the benefits of combining individual and social learning. While social learning yields consistent outcomes across agents, individual learning offers the possibility of higher rewards but also greater risks. A notable finding of this research is the development of asymmetrical influence patterns in social networks. This phenomenon refers to a tendency where certain agents become disproportionately influential in guiding others’ choices, leading to a centralization in how advice is sought and followed within the network. This aspect of the model sheds light on the nuances of social dynamics in decision-making processes. The study enhances our understanding of consumer behavior in markets for experience goods, providing insights into the complex interplay of individual experiences and social influences in shaping economic decisions.